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<p>We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[<sup>18</sup>F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Computed Tomography (PET/CT) studies, consisting of 1,683 PET/CT images and the corresponding CT-derived segmentations of 130 target regions. This multi-center dataset includes images from individuals without overt disease and patients with a range of malignant and inflammatory pathologies, including arthritis, lymphoma, and melanoma, as well as cancers of the lung, head-neck, and genito-urinary tract. Target regions were first automatically segmented from CT images using an in-house software and subsequently verified and corrected by physicians-in-training. In total, the segmented regions encompass 130 volumes, including abdominal organs, muscles, bones, cardiac subregions, vessels, adipose tissue, and skeletal muscle around the third lumbar vertebra. PET/CT images and corresponding CT-derived segmentations are provided in anonymized NIfTI format. The dataset can be used for deep learning training, validation, or multi-modality image analysis and thus fills an important gap in available resources to advance the use of PET/CT data in clinical management.</p>

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Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Daria Ferrara,
  • Manuel Pires,
  • Sebastian Gutschmayer,
  • Josef Yu,
  • Yasser G. Abdelhafez,
  • Elisabetta Abenavoli,
  • Ramsey D. Badawi,
  • Abhijit J. Chaudhari,
  • Moon S. Chen Jr.,
  • Simon R. Cherry,
  • Armin Frille,
  • Barbara K. Geist,
  • Stefan Gruenert,
  • Marcus Hacker,
  • Swen Hesse,
  • Teresa Kerkhoff,
  • Pia Linder,
  • Johanna Pappisch,
  • Smilla Pusitz,
  • Osama A. Raslan,
  • Ivo Rausch,
  • Siba P. Raychaudhuri,
  • Osama Sabri,
  • Fabian P. Schmidt,
  • Roberto Sciagrà,
  • Benjamin A. Spencer,
  • Guobao Wang,
  • Hubert Wirtz,
  • Thomas Beyer,
  • Lalith Kumar Shiyam Sundar

摘要

We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Computed Tomography (PET/CT) studies, consisting of 1,683 PET/CT images and the corresponding CT-derived segmentations of 130 target regions. This multi-center dataset includes images from individuals without overt disease and patients with a range of malignant and inflammatory pathologies, including arthritis, lymphoma, and melanoma, as well as cancers of the lung, head-neck, and genito-urinary tract. Target regions were first automatically segmented from CT images using an in-house software and subsequently verified and corrected by physicians-in-training. In total, the segmented regions encompass 130 volumes, including abdominal organs, muscles, bones, cardiac subregions, vessels, adipose tissue, and skeletal muscle around the third lumbar vertebra. PET/CT images and corresponding CT-derived segmentations are provided in anonymized NIfTI format. The dataset can be used for deep learning training, validation, or multi-modality image analysis and thus fills an important gap in available resources to advance the use of PET/CT data in clinical management.